CN115512244B - Method and system for determining carbon reserves of single tree - Google Patents
Method and system for determining carbon reserves of single tree Download PDFInfo
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Abstract
The invention provides a method and a system for determining carbon reserves of single tree, which relate to the technical field of calculation of carbon reserves of trees and comprise the following steps: acquiring global three-dimensional laser point cloud data of a region to be detected, which is obtained by scanning the region to be detected through an airborne laser scanning technology or an unmanned aerial vehicle laser scanning technology; performing single-wood segmentation on the global three-dimensional laser point cloud data to obtain a plurality of single-wood three-dimensional laser point cloud data, and compensating; and determining the laser radar biomass index of each tree in the to-be-detected area based on the plurality of single-tree three-dimensional laser point cloud compensation data, and further determining the forest aboveground biomass and the single-tree carbon reserves of each tree in the to-be-detected area. Therefore, the invention can determine the forest land biomass of each tree in a large-scale forest by utilizing the laser radar point cloud data obtained by the airborne laser scanning technology or the unmanned aerial vehicle laser scanning technology through data compensation, and further complete the tree carbon reserve distribution depiction of the forest.
Description
Technical Field
The invention relates to the technical field of tree carbon reserves calculation, in particular to a method and a system for determining carbon reserves of single tree.
Background
Forest above-ground biomass (AGB) estimation is the primary method to quantify carbon reserves and retention, as well as to evaluate climate change. Currently, ground-based lidar technology (Terrestrial Laser Scanning, TLS) combined with crown and treetop parameters of single-wood can accomplish accurate AGB estimation on a single-wood scale. However, TLS is a costly data acquisition modality that makes it difficult to implement large-area forest biomass mapping. Compared with TLS, the airborne laser scanning technology (Airborne Laser Scanning, ALS) or the unmanned aerial vehicle laser scanning technology (Unmanned aerial vehicle Laser Scanning, ULS) can accurately describe a large-scale forest canopy through the acquired three-dimensional laser point cloud, and three-dimensional structural parameters of each tree can be calculated by single-tree segmentation, so that a new opportunity is brought to biomass mapping of forests and even larger scales. However, unlike the observation view and the working mode of TLS, ALS and ULS only have accurate descriptions of the upper layers of crowns, and only a small amount of point clouds of the lower layers of crowns can be obtained by using the gaps of the forests under the influence of the shielding of the crowns of the forests. The severe absence of the cloud of points below the crown makes it difficult for the three-dimensional laser point clouds acquired by ALS and ULS to accurately calculate forest land biomass using the existing Lidar Biomass Index (LBI).
Disclosure of Invention
The invention aims to provide a method and a system for determining carbon reserves of single trees, which can determine the aboveground biomass of each tree in a large-scale forest by utilizing laser radar point cloud data obtained by an airborne laser scanning technology or an unmanned aerial vehicle laser scanning technology, so as to complete single-tree scale carbon reserves drawing of the forest.
In order to achieve the above object, the present invention provides the following solutions:
a method for determining carbon reserves of individual trees, comprising:
acquiring global three-dimensional laser point cloud data of a region to be measured; the global three-dimensional laser point cloud data are obtained by scanning an area to be detected through an airborne laser scanning technology or an unmanned aerial vehicle laser scanning technology;
performing single-wood segmentation on the global three-dimensional laser point cloud data to obtain single-wood three-dimensional laser point cloud data of each tree in the to-be-detected area;
according to the plurality of single-tree three-dimensional laser point cloud data, determining the tree height of each tree in the to-be-detected area;
respectively compensating the plurality of single-wood three-dimensional laser point cloud data to obtain a plurality of single-wood three-dimensional laser point cloud compensation data;
determining a laser radar biomass index of each tree in the to-be-detected area based on the tree height of each tree and the single tree three-dimensional laser point cloud compensation data;
determining forest land biomass of each tree according to the laser radar biomass index of each tree in the to-be-detected area;
and determining the carbon reserves of each tree in the area to be detected according to the forest above-ground biomass.
Optionally, after determining the carbon reserves of each tree in the area to be measured according to the forest above-ground biomass, the method further comprises:
and constructing a carbon reserve distribution map of the area to be detected according to the carbon reserve of each tree in the area to be detected.
Optionally, the performing single-tree segmentation on the global three-dimensional laser point cloud data to obtain single-tree three-dimensional laser point cloud data of each tree in the to-be-detected area includes:
clustering the global three-dimensional laser point cloud data by adopting a mean shift algorithm to obtain a plurality of super voxels;
processing a plurality of super voxels by adopting a K-nearest neighbor algorithm, constructing a similarity matrix, and performing a K-nearest neighbor based on the similarity matrixApproximating (Nistelom) to obtain a solution of a matrix to obtain a plurality of single-wood voxel clustering results;
and mapping each single-wood voxel clustering result into three-dimensional laser point cloud data to obtain single-wood three-dimensional laser point cloud data of each tree in the to-be-detected area.
Optionally, the compensating the plurality of single-wood three-dimensional laser point cloud data to obtain a plurality of single-wood three-dimensional laser point cloud compensation data includes:
determining any tree in the to-be-detected area as a current tree;
layering the current tree single-tree three-dimensional laser point cloud data by taking a preset interval height delta H as an interval to obtain multi-layer three-dimensional laser point cloud data;
determining the maximum cross-section height of the current tree based on the three-dimensional laser point cloud data of the layers;
acquiring the height of the lowest detection point of the current tree;
and compensating the single-tree three-dimensional laser point cloud data of the current tree based on the maximum cross section height and the minimum detection point height to obtain single-tree three-dimensional laser point cloud compensation data of the current tree.
Optionally, the determining the maximum cross-sectional height of the current tree based on the three-dimensional laser point cloud data of the layers includes:
calculating the area surrounded by three-dimensional laser point cloud data of each layer by adopting a Delaunay triangulation algorithm;
the center height of the layer with the largest area is determined to be the maximum cross-sectional height of the current tree.
Optionally, the compensating the single-tree three-dimensional laser point cloud data of the current tree based on the maximum cross-section height and the minimum detection point height to obtain single-tree three-dimensional laser point cloud compensation data of the current tree includes:
projecting layer three-dimensional laser point cloud data corresponding to the maximum cross section of the current tree on the same plane; the plane is parallel to the ground plane;
detecting boundary points of a plurality of projection points on the plane by using an alpha-shape algorithm;
fitting a plurality of boundary points by using a random sampling consistency algorithm to obtain a fitting circle;
taking the radius of the fitting circle as the radius of the bottom surface, taking the projection point of the single wood in the layer corresponding to the maximum cross section of the current tree as the center of the bottom surface, taking the difference between the height of the maximum cross section and the height of the lowest detection point as high, and constructing a cylindrical area below the layer corresponding to the maximum cross section of the current tree as an area to be compensated;
determining the point cloud data density above the maximum cross section height in the current tree single-tree three-dimensional laser point cloud data;
filling point cloud data in the area to be compensated according to the point cloud data density;
and determining the point cloud data above the maximum cross section height in the current tree single-tree three-dimensional laser point cloud data and the point cloud data filled in the area to be compensated as single-tree three-dimensional laser point cloud compensation data of the current tree.
Optionally, the laser radar biological index is:
wherein LBI is the laser radar biological index; h i Is the height value of the ith layer of the tree crown, H T Is the height of a single tree, U L (H) R (H) is the crown radius of a single tree with the height H, which is the distribution function of the area and the density of the single tree; Δh is the space height;
the forest land biomass is as follows:
wherein AGB is forest ground biomass; alpha, beta and k are all input parameters.
A single tree carbon reserve determination system comprising:
the global three-dimensional laser point cloud data acquisition module is used for acquiring global three-dimensional laser point cloud data of the region to be detected; the global three-dimensional laser point cloud data are obtained by scanning an area to be detected through an airborne laser scanning technology or an unmanned aerial vehicle laser scanning technology;
the single-wood segmentation module is used for carrying out single-wood segmentation on the global three-dimensional laser point cloud data to obtain single-wood three-dimensional laser point cloud data of each tree in the to-be-detected area;
the structural parameter determining module is used for determining the tree height of each tree in the to-be-detected area according to the plurality of single-tree three-dimensional laser point cloud data;
the point cloud data compensation module is used for respectively compensating the plurality of single-wood three-dimensional laser point cloud data to obtain a plurality of single-wood three-dimensional laser point cloud compensation data;
the laser radar biological index determining module is used for determining the laser radar biological index of each tree in the to-be-detected area based on the tree height of each tree and the single-tree three-dimensional laser point cloud compensation data;
the forest ground biomass determining module is used for determining the forest ground biomass of each tree according to the laser radar biomass index of each tree in the to-be-detected area;
and the single tree carbon reserve determining module is used for determining the carbon reserve of each tree in the to-be-detected area according to a plurality of forest biomass on the ground and tree species information.
Optionally, the system further comprises:
the tree carbon reserve distribution map construction module is used for constructing a carbon reserve distribution map of the area to be detected according to the carbon reserve of each tree in the area to be detected.
Optionally, the single wood splitting module includes:
the super-voxel determining unit is used for clustering the global three-dimensional laser point cloud data by adopting a mean shift algorithm to obtain a plurality of super-voxels;
a single wood voxel clustering unit for processing a plurality of super voxels by adopting a K-nearest neighbor algorithm, constructing a similarity matrix, and performing a clustering algorithm based on the similarity matrixObtaining a matrix solution to obtain a plurality of single-wood voxel clustering results;
the single-tree three-dimensional laser point cloud data acquisition unit is used for mapping each single-tree voxel clustering result into three-dimensional laser point cloud data to obtain single-tree three-dimensional laser point cloud data of each tree in the to-be-detected area.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a method and a system for determining carbon reserves of single trees, comprising the following steps: acquiring global three-dimensional laser point cloud data of a region to be measured; the global three-dimensional laser point cloud data are obtained by scanning an area to be detected through an airborne laser scanning technology or an unmanned plane laser scanning technology; performing single-tree segmentation on the global three-dimensional laser point cloud data to obtain single-tree three-dimensional laser point cloud data of each tree in the to-be-detected area; respectively compensating the plurality of single-wood three-dimensional laser point cloud data to obtain a plurality of single-wood three-dimensional laser point cloud compensation data; and determining the laser radar biomass index of each tree in the to-be-detected area based on the plurality of single-tree three-dimensional laser point cloud compensation data, and further determining the forest aboveground biomass and carbon reserves of each tree in the to-be-detected area. Therefore, the invention can determine the forest land biomass of each tree in a large-scale forest by utilizing the laser radar point cloud data obtained by the airborne laser scanning technology or the unmanned aerial vehicle laser scanning technology through data compensation, and further complete the tree carbon reserve distribution depiction of the forest.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for determining carbon reserves of a single tree in an embodiment of the invention;
FIG. 2 is a schematic diagram of compensation of single-wood three-dimensional laser point cloud data in an embodiment of the present invention;
FIG. 3 is a schematic diagram showing the variation rule of the mean value and standard deviation of model parameters with the number of single wood samples according to the embodiment of the present invention; FIG. 3 (a) is a schematic diagram showing a variation rule of average values of parameters with the number of samples according to an embodiment of the present invention; FIG. 3 (b) is a schematic diagram showing the variation rule of standard deviation of each parameter with the number of samples in the embodiment of the present invention;
FIG. 4 is a regression chart of the AGB estimated by different models and the AGB actually measured by the analytical wood in the embodiment of the present invention; FIG. 4 (a) is a regression diagram of biomass obtained by analyzing a wood model and measured biomass according to an embodiment of the invention; FIG. 4 (b) is a regression diagram of biomass obtained by selecting a single wood model and measured biomass according to an embodiment of the invention; FIG. 4 (c) is a regression diagram of biomass and measured biomass obtained by analyzing a wood and selecting a single wood fusion model in an embodiment of the invention; FIG. 4 (d) is a regression plot of calculated biomass versus measured biomass based on LiDAR predicted DBH in an embodiment of the invention;
fig. 5 is a graph showing a tree carbon reserve profile of a test zone in accordance with an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method and a system for determining carbon reserves of single trees, which can determine forest land biomass of each tree in a large-scale forest by utilizing laser radar point cloud data obtained by an airborne laser scanning technology or an unmanned aerial vehicle laser scanning technology, and further finish tree carbon reserve distribution depiction of the forest.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Examples
As shown in fig. 1, the present invention provides a method for determining carbon reserves of individual trees, comprising:
step 101: acquiring global three-dimensional laser point cloud data of a region to be measured; the global three-dimensional laser point cloud data is obtained by scanning an area to be detected through an airborne laser scanning technology or an unmanned aerial vehicle laser scanning technology.
Step 102: and performing single-wood segmentation on the global three-dimensional laser point cloud data to obtain single-wood three-dimensional laser point cloud data of each tree in the to-be-detected area.
Step 102 comprises:
step 1021: and clustering the global three-dimensional laser point cloud data by adopting a means shift algorithm to obtain a plurality of super voxels.
Step 1022: processing a plurality of super voxels by adopting a K-nearest neighbor algorithm, constructing a similarity matrix, and performing a K-nearest neighbor based on the similarity matrixThe approximation of the matrix is obtained, and a plurality of single wood voxel clustering results are obtained.
Step 1023: and mapping each single-wood voxel clustering result into three-dimensional laser point cloud data to obtain single-wood three-dimensional laser point cloud data of each tree in the to-be-detected area.
In step 102, the method is based onSpectral clustering of (+)>-based spectral clustering, NSC) method for single-wood segmentation of an airborne point cloud. Firstly, clustering the normalized laser radar point cloud by adopting a mean shift method, and converting the normalized laser radar point cloud into super voxels with different sizes so as to reduce the data quantity and improve the calculation efficiency. Then, a similarity graph based on voxels is constructed by adopting a K-Nearest Neighbor (KNN) method, and the relation among the voxels is drawn by adopting a sparse adjacency matrixBy being based on->To obtain a solution to the matrix. At the same time based on->The K-nearest neighbor based sampling (K-Nearest Neighbor sampling, KNNS) method was introduced to reduce the computational pressure. And finally, determining the number of the segmented single wood by combining the calculated characteristic values and the characteristic vectors by using a characteristic gap heuristic method, and obtaining each segmented single wood point cloud set and single wood structure parameters by mapping the result back to the original point cloud. And further carrying out post-processing on the segmentation result based on the single-tree structure parameters to improve the segmentation precision, and updating the tree height, crown amplitude and coordinates of each single-tree according to the final segmentation result.
Step 103: and determining the tree height of each tree in the to-be-detected area according to the plurality of single-tree three-dimensional laser point cloud data.
Step 104: and respectively compensating the plurality of single-wood three-dimensional laser point cloud data to obtain a plurality of single-wood three-dimensional laser point cloud compensation data.
Step 104 comprises:
step 1041: and determining any tree in the to-be-detected area as the current tree.
Step 1042: layering the current tree single-tree three-dimensional laser point cloud data by taking the preset interval height delta H as an interval to obtain multi-layer three-dimensional laser point cloud data.
Step 1043: based on the multi-layer three-dimensional laser point cloud data, a maximum cross-sectional height of the current tree is determined.
Step 1043 includes:
step 10431: and calculating the area surrounded by the three-dimensional laser point cloud data of each layer by adopting a Delaunay triangulation algorithm.
Step 10432: the center height of the layer with the largest area is determined to be the maximum cross-sectional height of the current tree.
Step 1044: and (5) projecting the Shan Mudian cloud, selecting points in the range of the boundary buffer zone of the maximum cross section, and obtaining the height of the lowest detection point of the current tree.
Step 1045: and compensating the single-wood three-dimensional laser point cloud data of the current tree based on the maximum cross section height and the minimum detection point height to obtain the single-wood three-dimensional laser point cloud compensation data of the current tree.
Step 1045 includes:
step 10451: projecting layer three-dimensional laser point cloud data corresponding to the maximum cross section of the current tree on the same plane; the plane is parallel to the ground plane.
Step 10452: and detecting boundary points of a plurality of projection points on the plane by using an alpha-shape algorithm.
Step 10453: and fitting the boundary points by using a random sampling consistency algorithm to obtain a fitting circle.
Step 10454: the radius of the fitting circle is taken as the radius of the bottom surface, the projection point of the single wood in the layer corresponding to the maximum cross section of the current tree is taken as the center of the bottom surface, the difference between the height of the maximum cross section and the height of the lowest detection point is taken as high, and a cylindrical area is constructed below the layer corresponding to the maximum cross section of the current tree as an area to be compensated.
Step 10455: and determining the density of the point cloud data above the maximum cross-section height in the current tree single-tree three-dimensional laser point cloud data.
Step 10456: filling point cloud data in the area to be compensated according to the point cloud data density.
Step 10457: and determining the point cloud data above the maximum cross section height in the current tree single-tree three-dimensional laser point cloud data and the point cloud data filled in the area to be compensated as single-tree three-dimensional laser point cloud compensation data of the current tree.
As shown in fig. 2, the lowest crown detection point (Lowest crown detection point, LCDP) of als to an individual tree is typically located on an effective shoot, which can indirectly represent the height of the effective crown. Meanwhile, for conifer species, there is generally a similar spatial structure from the highest point of each individual wood to a high maximum cross-sectional area (Hcmax). Therefore, the invention provides a method for acquiring the complete point cloud from the maximum cross-sectional area of the single-tree crown to the lowest detection point by using a cylindrical simulation method. The method comprises the following specific steps:
first, the maximum cross-sectional height of the individual crown is automatically calculated. The airborne point clouds of each tree are divided according to a specified height interval delta H (comprehensively considering the point cloud density and the tree height, delta H is generally defined as 0.5 m), the area of each layer is calculated by Delaunay triangulation, and then the height Hcmax of the layer with the largest cross-sectional area is selected.
Secondly, detecting the lowest crown detection point of each single tree. All the crown point clouds in the layer with the height Hcmax of the layer with the largest cross-sectional area are projected to an XOY plane, the boundary points of the projected points are detected by adopting an alpha-shape algorithm, and then fitting is carried out by combining a random sampling consistency algorithm (Random sampling consistency, RANSAC) with a round model to obtain the center point of the circle as a datum point P. If the radius of the circle obtained by fitting is R, the lowest point in the points with the plane distance within the range of R+ -delta (delta=R/3 according to the empirical value) from the reference point P is the lowest crown detection point LCDP, and the lowest crown detection point height is H LCDP 。
Finally, the circle obtained by fitting is taken as the base, and Hcmax-H is taken as the base LCDP And (3) taking the average point cloud density of each single wood Hcmax or more as the point density of the simulation points to perform point cloud compensation. The simulation point cloud and the original point cloud above Hcmax participate in the calculation of the single wood LBI together.
Step 105: determining a laser radar biomass index of each tree in the to-be-detected area based on the tree height of each tree and the single tree three-dimensional laser point cloud compensation data;
the laser radar biological index is:
wherein LBI is the laser radar biological index; h i Is the height value of the ith layer of the tree crown, H T Is the height of a single tree, U L (H) Is a single leaf area bulk density distribution function, and the unit is m 2 /m 3 The method comprises the steps of carrying out a first treatment on the surface of the r (H) is the crown radius of the individual tree with the height H; ΔH is the space height.
Step 106: and determining the forest aboveground biomass of each tree in the area to be detected according to the laser radar biomass indexes.
Forest land biomass is:
wherein AGB is forest ground biomass; alpha, beta and k are all input parameters.
According to the method, a plurality of single woods with higher segmentation precision and better matching effect with measured data are selected from single wood segmentation results, and a reference AGB is calculated according to a measured DBH (single wood chest diameter) and combined with a different-speed growth equation, so that regression acquisition parameters are obtained by replacing analysis of the measured AGB of the single woods. The determination of the number of sample single woods is a precondition for model parameter regression. When the point cloud density meets the single-wood segmentation requirement, the forest with low plant number density can be segmented and matched with higher precision. Therefore, the method aims to select 70 single trees with high single tree segmentation precision, wide DBH distribution range and complete matching with measured data from needle leaf forest point clouds with low plant number density. First, all the single woods were classified into 10 grades according to the actual measured DBH, so that 7 single woods were included in each grade. Then, LBI and tree height of each individual tree are calculated according to the method proposed by the present invention, and reference AGB is calculated in combination with measured DBH. Finally, by selecting 1 to 7 single woods in each level to constitute a sample set and performing parameter regression, the decision coefficient R between the parameters obtained for the same sample number is analyzed 2 And root mean square error (RMSE: root mean square error) (the law of variation of values and standard deviation with the number of sample single-woods is shown in fig. 3), thereby determining the minimum number of sample single-woods required for parameter regression, and greatly reducing the workload of field measurement on the basis of selecting a proper sample single-woods.
Step 107: and determining the carbon reserves of each tree in the area to be detected according to the biomass on the forest lands and the tree species information and the carbon density.
Step 108: constructing a carbon reserve distribution map of the area to be detected according to the carbon reserve of each tree in the area to be detected; the carbon reserves profile is shown in fig. 5.
According to the invention, the Shan Mushu coronary point cloud obtained by the airborne laser radar is subjected to analog compensation, so that the laser radar biomass index is successfully applied to the data, and the accurate estimation of forest biomass of single wood scale is realized. The pair of accuracy before and after compensation is shown in table 2.
Meanwhile, the parameter regression of the biomass estimation model can be realized by combining a small amount of sample structure parameters measured in the field with a general abnormal-speed growth equation, so that the workload of field measurement is greatly reduced. The existing single wood biomass estimation based on the airborne laser radar data is often realized by indirectly acquiring single wood DBH based on tree height and crown width and combining with a different-speed growth equation. As shown in fig. 4, the biomass is directly calculated by comprehensively utilizing the shape and scale characteristics of the tree height and the tree crown, the accumulation of multiple errors is reduced on the basis of increasing the utilization rate of the characteristic of the tree crown, and the biomass calculation with higher precision can be realized. Meanwhile, biomass estimation for plots, forests and larger scales can be achieved by simple single wood superposition.
As shown in fig. 3 (a) and 3 (b), the increase of the number of samples used for parameter regression does not have a significant effect on the mean value of the parameters, but the standard deviation between the regression parameters shows a significant decrease trend with the increase of the number of samples, which indicates that the parameter regression is easily performed by less sample logs, so that larger random errors are easily generated, and the stable calculation model can be generated by selecting more logs for parameter regression. The variations in a, β and κ become substantially constant when the number of samples reaches 21, and the standard deviation of each parameter reaches a minimum when the number of samples reaches 35. Thus, regression of 35 single woods selected from the single wood segmentation results for the model parameters was finally determined. Table 1 model parameters obtained by single wood regression of different samples figure 4 is a regression of biomass of 20 resolved woods calculated using the parameters in table 1 and measured biomass obtained using the destructive harvesting method. Meanwhile, all the analysis woods are divided into dominant woods, inferior woods and average woods in the collection process, and the analysis woods are respectively represented by symbols in different forms in a scatter diagram. As can be seen from FIGS. 4 (a) -4 (d), model parameter regression by parsing wood, R 2 ＝0.97，RMSE＝11.66kg，rRMSE＝16.33％；Selecting 35 samples of single wood from single wood segmentation results to perform model parameter regression, R 2 =0.98, rmse=15.93 kg, rrmse=22.31%, R compared to the results obtained by analysis of the wood regression model 2 Increased by 0.01, increased rmse by 4.27kg, increased rrmse by 5.98%; comprehensively adopting analytic wood and selective single wood to carry out model parameter regression, R 2 =0.98, rmse=11.88 kg, rrmse=16.64%. In general, the model obtained by analyzing the wood regression is closer to 1:1, but the single wood model is selected to obtain higher calculation accuracy. Method of predicting DBH in comparison to Shan Mushu high and coronary amplitude obtained from LiDAR, combined with biomass calculated by the abnormal growth model (R 2 =0.95, rmse=15.89 kg, rrmse=22.25%) has significant accuracy advantages.
Table 2 shows the accuracy comparison of biomass calculated using LBI with the regression of measured biomass before and after compensation of Shan Mushu coronary point cloud. It can be seen that the calculation accuracy of each model is obviously improved by compensating the crown point cloud, R 2 The average increase was 0.06, the average decrease in rmse was 17.77kg, and the average decrease in rrmse was 24.9%. Therefore, the method provided by the invention can be completely applied to the airborne laser radar to estimate the biomass of the single wood scale.
Fig. 5 shows a region-scale single wood carbon reserve profile. The biomass of each single wood obtained through calculation is multiplied by the carbon content of the corresponding forest stand to obtain the carbon storage of each single wood, so that a single wood carbon storage distribution map of an area scale is obtained, wherein the lower left corner enlarges and displays the part of the carbon storage distribution map, each pixel represents the position of the single wood, and the value of the pixel is the carbon storage of the corresponding single wood.
TABLE 1 model parameter Table obtained by Single Wood regression of different samples
Table 2. Shan Mushu accuracy comparison Table before and after Point cloud Compensation
In addition, the invention also provides a single tree carbon reserve determining system, which comprises:
the global three-dimensional laser point cloud data acquisition module is used for acquiring global three-dimensional laser point cloud data of the region to be detected; the global three-dimensional laser point cloud data is obtained by scanning an area to be detected through an airborne laser scanning technology or an unmanned aerial vehicle laser scanning technology.
And the single-wood segmentation module is used for carrying out single-wood segmentation on the global three-dimensional laser point cloud data to obtain single-wood three-dimensional laser point cloud data of each tree in the to-be-detected area.
And the structural parameter determining module is used for determining the tree height of each tree in the to-be-detected area according to the plurality of single-tree three-dimensional laser point cloud data.
And the point cloud data compensation module is used for respectively compensating the plurality of single-wood three-dimensional laser point cloud data to obtain a plurality of single-wood three-dimensional laser point cloud compensation data.
The laser radar biological index determining module is used for determining the laser radar biological index of each tree in the to-be-detected area based on the tree height of each tree and the single-tree three-dimensional laser point cloud compensation data.
The forest ground biomass determining module is used for determining the forest ground biomass of each tree according to the laser radar biomass index of each tree in the to-be-detected area;
and the single tree carbon reserve determining module is used for determining the carbon reserve of each tree in the to-be-detected area according to a plurality of forest biomass on the ground and tree species information.
The tree carbon reserve distribution map construction module is used for constructing a carbon reserve distribution map of the area to be detected according to the carbon reserve of each tree in the area to be detected.
Wherein, the single wood splitting module includes:
the super-voxel determining unit is used for clustering the global three-dimensional laser point cloud data by adopting a mean shift algorithm to obtain a plurality of super-voxels;
a single wood voxel clustering unit for processing a plurality of super voxels by adopting a K-nearest neighbor algorithm, constructing a similarity matrix, and performing a clustering algorithm based on the similarity matrixObtaining a matrix solution to obtain a plurality of single-wood voxel clustering results;
the single-tree three-dimensional laser point cloud data acquisition unit is used for mapping each single-tree voxel clustering result into three-dimensional laser point cloud data to obtain single-tree three-dimensional laser point cloud data of each tree in the to-be-detected area.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In summary, the present description should not be construed as limiting the invention.
Claims (7)
1. A method for determining carbon reserves of individual trees, comprising the steps of:
acquiring global three-dimensional laser point cloud data of a region to be measured; the global three-dimensional laser point cloud data are obtained by scanning an area to be detected through an airborne laser scanning technology or an unmanned aerial vehicle laser scanning technology;
single-wood separation of the global three-dimensional laser point cloud dataCutting to obtain single wood three-dimensional laser point cloud data of each tree in the to-be-detected area, wherein the method specifically comprises the following steps: clustering the global three-dimensional laser point cloud data by adopting a mean shift algorithm to obtain a plurality of super voxels; processing a plurality of super voxels by adopting a K-nearest neighbor algorithm, constructing a similarity matrix, and performing a K-nearest neighbor based on the similarity matrixObtaining a matrix solution to obtain a plurality of single-wood voxel clustering results; mapping each single-wood voxel clustering result into three-dimensional laser point cloud data to obtain single-wood three-dimensional laser point cloud data of each tree in the to-be-detected area;
according to the plurality of single-tree three-dimensional laser point cloud data, determining the tree height of each tree in the to-be-detected area;
respectively compensating the plurality of single-wood three-dimensional laser point cloud data to obtain a plurality of single-wood three-dimensional laser point cloud compensation data, which specifically comprises the following steps: determining any tree in the to-be-detected area as a current tree; layering the current tree single-tree three-dimensional laser point cloud data by taking a preset interval height delta H as an interval to obtain multi-layer three-dimensional laser point cloud data; determining the maximum cross-section height of the current tree based on the three-dimensional laser point cloud data of the layers; acquiring the height of the lowest detection point of the current tree; compensating the single-tree three-dimensional laser point cloud data of the current tree based on the maximum cross section height and the minimum detection point height to obtain single-tree three-dimensional laser point cloud compensation data of the current tree;
determining a laser radar biomass index of each tree in the to-be-detected area based on the tree height of each tree and the single tree three-dimensional laser point cloud compensation data;
the laser radar biological index is as follows:
wherein LBI is the laser radar biological index; h i Is the height value of the ith layer of the tree crown, H T Is the height of a single tree, U L (H) Is a single leafAn area volume density distribution function, r (H) is the crown radius of a single tree with the height H; Δh is the space height;
the forest land biomass is as follows:
wherein AGB is forest ground biomass; alpha, beta and k are all input parameters;
determining the forest aboveground biomass of each tree according to the laser radar biomass index of each tree in the areas to be detected;
and determining the carbon reserves of each tree in the area to be detected according to the forest above-ground biomass.
2. The method for determining carbon reserves of individual trees of claim 1, further comprising, after said determining the carbon reserves of each tree in the area to be measured from a plurality of said forest land biomass:
and constructing a carbon reserve distribution map of the area to be detected according to the carbon reserve of each tree in the area to be detected.
3. The method of determining carbon reserves of an individual tree of claim 1, wherein the determining the maximum cross-sectional height of the current tree based on the plurality of layer three-dimensional laser point cloud data comprises:
calculating the area surrounded by three-dimensional laser point cloud data of each layer by adopting a Delaunay triangulation algorithm;
the center height of the layer with the largest area is determined to be the maximum cross-sectional height of the current tree.
4. The method for determining carbon reserves of a single tree according to claim 3, wherein the compensating the single tree three-dimensional laser point cloud data of the current tree based on the maximum cross-sectional height and the minimum detection point height to obtain the single tree three-dimensional laser point cloud compensation data of the current tree comprises:
projecting layer three-dimensional laser point cloud data corresponding to the maximum cross section of the current tree on the same plane; the plane is parallel to the ground plane;
detecting boundary points of a plurality of projection points on the plane by using an alpha-shape algorithm;
fitting a plurality of boundary points by using a random sampling consistency algorithm to obtain a fitting circle;
taking the radius of the fitting circle as the radius of the bottom surface, taking the projection point of the single wood in the layer corresponding to the maximum cross section of the current tree as the center of the bottom surface, taking the difference between the height of the maximum cross section and the height of the lowest detection point as high, and constructing a cylindrical area below the layer corresponding to the maximum cross section of the current tree as an area to be compensated;
determining the point cloud data density above the maximum cross section height in the current tree single-tree three-dimensional laser point cloud data;
filling point cloud data in the area to be compensated according to the point cloud data density;
and determining the point cloud data above the maximum cross section height in the current tree single-tree three-dimensional laser point cloud data and the point cloud data filled in the area to be compensated as single-tree three-dimensional laser point cloud compensation data of the current tree.
5. A single tree carbon reserve determination system, comprising:
the global three-dimensional laser point cloud data acquisition module is used for acquiring global three-dimensional laser point cloud data of the region to be detected; the global three-dimensional laser point cloud data are obtained by scanning an area to be detected through an airborne laser scanning technology or an unmanned aerial vehicle laser scanning technology;
the single wood segmentation module is used for carrying out single wood segmentation on the global three-dimensional laser point cloud data to obtain single wood three-dimensional laser point cloud data of each tree in the to-be-detected area, and specifically comprises the following steps: clustering the global three-dimensional laser point cloud data by adopting a mean shift algorithm to obtain a plurality of super voxels; processing a plurality of super voxels by adopting a K-nearest neighbor algorithm, constructing a similarity matrix, and performing a K-nearest neighbor based on the similarity matrixObtaining a matrix solution to obtain a plurality of single-wood voxel clustering results; mapping each single-wood voxel clustering result into three-dimensional laser point cloud data to obtain single-wood three-dimensional laser point cloud data of each tree in the to-be-detected area;
the structural parameter determining module is used for determining the tree height of each tree in the to-be-detected area according to the plurality of single-tree three-dimensional laser point cloud data, and specifically comprises the following steps: determining any tree in the to-be-detected area as a current tree; layering the current tree single-tree three-dimensional laser point cloud data by taking a preset interval height as an interval to obtain multi-layer three-dimensional laser point cloud data; determining the maximum cross-section height of the current tree based on the three-dimensional laser point cloud data of the layers; acquiring the height of the lowest detection point of the current tree; based on the maximum cross-sectional height and the minimum detection point height;
the point cloud data compensation module is used for respectively compensating the plurality of single-wood three-dimensional laser point cloud data to obtain a plurality of single-wood three-dimensional laser point cloud compensation data;
the laser radar biological index determining module is used for determining the laser radar biological index of each tree in the to-be-detected area based on the tree height of each tree and the single-tree three-dimensional laser point cloud compensation data;
the laser radar biological index is as follows:
wherein LBI is the laser radar biological index; h i Is the height value of the ith layer of the tree crown, H T Is the height of a single tree, U L (H) R (H) is the crown radius of a single tree with the height H, which is the distribution function of the area and the density of the single tree; Δh is the space height;
the forest land biomass is as follows:
wherein AGB is forest ground biomass; alpha, beta and k are all input parameters;
the forest ground biomass determining module is used for determining the forest ground biomass of each tree according to the laser radar biomass index of each tree in the to-be-detected area;
and the single tree carbon reserve determining module is used for determining the carbon reserve of each tree in the to-be-detected area according to a plurality of forest biomass on the ground and tree species information.
6. The individual tree carbon reserve determination system of claim 5, further comprising:
the tree carbon reserve distribution map construction module is used for constructing a carbon reserve distribution map of the area to be detected according to the carbon reserve of each tree in the area to be detected.
7. The individual tree carbon reserve determination system of claim 5, wherein the individual tree segmentation module comprises:
the super-voxel determining unit is used for clustering the global three-dimensional laser point cloud data by adopting a mean shift algorithm to obtain a plurality of super-voxels;
a single wood voxel clustering unit for processing a plurality of super voxels by adopting a K-nearest neighbor algorithm, constructing a similarity matrix, and performing a clustering algorithm based on the similarity matrixObtaining a matrix solution to obtain a plurality of single-wood voxel clustering results;
the single-tree three-dimensional laser point cloud data acquisition unit is used for mapping each single-tree voxel clustering result into three-dimensional laser point cloud data to obtain single-tree three-dimensional laser point cloud data of each tree in the to-be-detected area.
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